Eatomics

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Eatomics is an R-Shiny based web application that enables interactive exploration of quantitative proteomics data generated by MaxQuant software. Eatomics enables fast exploration of differential expression and pathway analysis to researchers with limited bioinformatics knowledge. The application aids in quality control of the quantitative proteomics data, visualization, differential expression and pathway analysis. Highlights of the application are an extensive experimental setup module, the data and report generation feature and the multiple ways to interact and customize the analysis.

1. Input files

Eatomics requires two file inputs:

1. Demo_proteinGroups.txt: The proteinGroups.txt (i.e. a tab-separated files) as generated by the quantitative analysis software of raw mass spectrometry data - MaxQuant. The file should contain at least the columns Protein IDs, Majority protein IDs, Gene names, LFQ/iBAQ measurement columns, Reverse, Potential contaminant, Only identified by site. The latter three may be empty.

2. Demo_clinicaldata.txt: The sample description file - a tab separated text file as can be produced with any Office program by saving the spread sheet as .txt. The file needs to contain a column named “PatientID”, which contains IDs that match the sample ID’s from the proteinGroups header (without the “LFQ intensity” or “iBAQ” prefixes) and one or more named columns with “parameters”, i.e. textual/factual/logical or continuous/integer values. Column names have to be unique.

The name of the samples shall be identical in both input files. Also, represent character variables as ‘factor’ and integers as ‘numeric’. Please note that ordinal values are treated as numeric. text

Access to demo data is possible directly via the upload button if ou are testing on our public server. For your local installation you may directly use your own data or the demo files in Eatomics/Data from the github repository.

2. Application walk-through

Eatomics functionality is structured into four tab panels:

• Load and Prepare raw data on samples and MaxQuant output, as well as quality control.
• Conduct differential expression analysis.
• Calculate enrichment scores per sample (ssGSEA).
• Conduct differential enrichment analysis.

All tabs consist of a side panel to configure the analysis and a main panel for interactive analysis visualization.

Step 1: Load and Prepare

The first tab provides an overview on the data quality and enables filtering and preparation of data for differential expression and enrichment analysis ().

Configuration panel

Within the side panel the user can load data and configure quality control options.

Load proteinGroups.txt input file

To begin the analysis the user has to upload the MaxQuant file (e.g.proteinGroups.txt), as specified above. After full upload of the file, rows that were only found in the reverse database, belonging to potential contaminants or that have only been identified by site are filtered automatically.

Quality control and data cleansing

• The user selects either LFQ (Label-free quantification) or iBAQ (Intensity Based Absolute Quantification) as intensity metric to be considered for succeeding differential expression analysis. If available, we suggest to use LFQ intensities as Eatomics was optimized for these. Internally, the intensity widget uses the selectProteinData function.

• The exclude column widget allows the user to exclude samples, especially if any outliers are found while conducting initial quality analysis such as PCA. Selecting a sample here, results in the removal of that sample from the consecutive steps analysis steps.

• To avoid proteins with many missing values across the samples, the user selects the minimum number of samples for which a protein must have been detected in. Internally the filter widget uses the filterProteins function.

• Meaningful gene names: As genes names are easier to interpret than peptide identifiers, the gene names are displayed primarily. As gene names can be non-unique, the user can choose to let Eatomics
  • prepare unique IDs for duplicate gene names or (make.unique() R base function)
  • to sum up multiple abundance values for one gene name (checkForIsoforms custom function). In the latter case, the user is informed about intensity shares.

• Missing value imputation can be performed using knn (k-nearest-neighbour), MinDet or QRLIC from the imputeLCMD package or a custom implementation of Perseus’ sampling from Gaussian distribution (implemented by Matthias Ziehm).

Load the sample description/clinical data file

Select and load the clinical data input file (e.g clinicaldata.txt), as specified above.

Configuration panel to load input data and to prepare the data set for analysis.

Visualization panel

In the main panel (right) interactive visualizations are shown.

Principal component analysis

A common method of dimensionality reduction is principal component analysis (PCA). Inherently, PCA calculates axes of most variation (principal components) within the expression data. A common assumption is that a plot along the axes of most variation will segregate all samples/patients into groups under investigation. The user can choose which principle components to visualize in the PCA and can choose to color the samples based on the uploaded sample/clinical characteristics.

Visualization of protein abundance in a PCA.

Distribution overview

Protein numbers describes the count of distinct proteins or isoforms per sample. The plot is generated by the plot_numbers() function from the DEP package which was adjusted to work without experimental design information.

Sample wise distribution overview of protein abundance data.

Protein coverage

Protein numbers describes the count of distinct protein groups per sample.

Sample-wise coverage of protein abundance data.

Sample to sample heatmap

The sample-to-sample heatmap describes the biological and/or technical variability of the samples. The user can choose to use Euclidean distance or Pearson correlation as a (dis-)similarity metric. Formed clusters should resemble the sample groups under investigation.

Sample to sample heatmap.

Cumulative Protein Intensities

Protein intensities are cumulated across all samples and plotted according to their relative abundance. Colouring marks the respective quantile of the proteins. Highly abundant proteins, i.e., proteins ranked in the first quartile are colored in red and labels are specified. The top 20 ranked proteins and their cumulated intensity are given in the table to the right.

Cumulative protein intensities of all samples.

Step 2: Differential expression

In step 2, the user is enabled to translate a given hypothesis on the data into an experimental design and to test the hypothesis.
Eatomics uses limma to perform real time analysis of differentially expressed proteins amongst clinical parameters of choice. The resulting interactive visualization plot including volcano plots (detailed below) allows a quick and detailed overview on the differential expression. limma (linear models for microarray data), is a commonly used R/Bioconductor software package for analyzing microarray and RNA-seq data. Limma functions fit statistical linear models and conduct differential expression analysis.

Experimental design configuration

• The clinical group widget allows the user to select the clinical parameter of their choice from the uploaded dataset, to perform limma analysis.

• The compare widget shows all the available subgroups inside the selected clinical group. The user then selects two subgroups to compare and find the differential expressed genes in the selected subgroups.

• The surrogate variable widget uses the sva{sva} function to remove the unknown source of variation such as batch effects from the expression value of the compared clinical subgroup. It is better to impute before, checking surrogate variable, as the sva function works better with non-missing values

• The impute values provides the user the flexibility to choose the imputed MaxQuant expression values (from the Load and Prep tab)

• The apply filters widget allows to extend filter to a second clinical parameter, where the user can narrow down the filtering to very specific subgroup of another clinical parameter of interest

• Using threshold box widget, the user can interactively adjust for multiple testing corrected (adjusted) P-value and log FoldChange(logFC) , plotting the genes above the chosen threshold in the volcano plot

Visualization of differentially expressed proteins

Volcano plot

lmFit{limma} along with other liner model functions from limma performs the differential expression analysis on the selected clinical group. Later, the resulting table to top-ranked genes{limma} the volcanoplot is plotted. In the plot, the x axis plots the log-2-fold change value (`lfc´ from the table) where logFC> 0 means the genes are up-regulated and logFC<0 meaning genes down regulated in the clinical group selected first (i.e,cases in Fig.5.) and the y axis plots the -log10 of P value (´p.value´) from the gene list.

Up and down regulated data table

The list of the genes up and down regulated (once that crosssed the given threshold) and their summary including the gene name, log-2-foldchange, P value and adjusted P value (adj.P.Val) is represented in the respective up and down regulated tables.

Analysis details

The collapsible panel contains details on the input paramaters that were selected by the user through the limma widgets.

Step 3: Enrichment score calculation (ssGSEA)

The tab provides an easy to use user-interface on the established (ssGSEA) (single-sample Gene Set Enrichment Analysis) method which is an extension of GSEA. The interactive widgets in the tab are developed based on the freely accessed codes developed by Broad institute ¹. The tab currently focuses only on four MSigDB(version, v6.1) genesets namely, H- hallmark geneset, C2- KEGG geneset, C5- GO geneset and C1- positional geneset to calculate the enrich ment score and the user selects one geneset from these (similar to conventional GSEA).

Later, the resulting output,the .gct files with the respective geneset enrichment score will be auto-downloaded into the working directory, into the folder called EnrichmentScore.

A message alert will be pop-up on download completion.

Screenshot of the ssGSEA widgets

Step 4: Differential enrichment

Differential expression analysis of the pathways enriched via ssGSEA (using the previou tabpanel) in the clinical group of interest,is facilitated by the tab. The pathway enrichment scores geneset is tested for differential pathway expression using t.test. The test compares the enriched pathways differential expressed among two clinical conditions selected.

Experimental design configuration

• The Gene Set Collection widget allows the user to select the geneset of his choice (from the available). On selecting the geneset, the file is auto-uploaded from EnrichmentScore folder, provided the user has saved the file using ssGSEA tab.

The score file is .gct file, a m x n matrix where m rows contain the name of the pathway from the selected geneset and n number of column represents the sample names matching to the clinical data.
Make sure the sample names (column names) format in the geneset collection matches to the sample names (PatientID) in the clinical data file. text

*Rest of the widgets in the panel follows the same pattern in the ‘differential limma’ tab

Visualization of differentially enriched pathways and gene sets

Volcano plot

The interactive volcanoplot plots the log ratio (x-axis) vs -log10 of P value calsulated based of t.test. The bonferroni adjusted P value (adj.p< 0.05) is selected as the threshold to highlight the differentially expressed pathways.

The other two result formats including interactive data table and the detailed description panel lists the highlighted up and and down regulated pathways and details on the input parameters, respecively.